def testTestfiles(self):
from create_testfiles import createMsssInput
std1_goal=1.
std2_goal=2.
corr_goal=0.5
std_ratio = std2_goal/std1_goal
cond_bias = corr_goal - std1_goal/std2_goal
msss_goal = corr_goal**2 + cond_bias**2
length = 50
(hindcasts,observations) = createMsssInput(1960, std1_goal, std2_goal, corr_goal, length)
msssDict = self.msssDict()
msssDict['decadals'] = hindcasts.keys()
msssDict['observation'] = 'hadcrut3v'
msssDict['leadtimes'] = '1'
msss = Msss(**msssDict)
msss.input1Remapped = hindcasts
msss.input2Remapped = hindcasts
msss.observationRemapped = observations
msss.analyze()
goal_dict = {'msss_goal': '1-1/baseline1_output_mpi-esm-lr_decs4e_input1/accuracy/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_msss.nc',
'corr_goal': '1-1/baseline1_output_mpi-esm-lr_decs4e_input1/accuracy/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_correlation.nc',
'cond_bias': '1-1/baseline1_output_mpi-esm-lr_decs4e_input1/accuracy/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_conditional_bias.nc',
'std_ratio': '1-1/baseline1_output_mpi-esm-lr_decs4e_input1/accuracy/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_std_ratio.nc'}
for key,val in goal_dict.iteritems():
t1 = FileHandler.openNetCDFFile(msss.outputDir+val,mode='var')
self.assertAlmostEqual(np.round(t1[12,12],2), locals()[key], 1)
def testMsss(self):
msssDict = self.msssDict()
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
#test file names
res_files = [f for r,_,files in os.walk(self.tmp_dir+'/output') for f in files]
test_files = [f for r,_,files in os.walk(self.test_path+'/output') for f in files]
self.assertEqual(sorted(res_files),sorted(test_files))
res_files = [f for r,_,files in os.walk(self.tmp_dir+'/plots') for f in files]
test_files = [f for r,_,files in os.walk(self.test_path+'/plots') for f in files]
self.assertEqual(sorted(res_files),sorted(test_files))
#test values of all .nc files
res_files = sorted([os.path.join(r,f) for r,_,files in os.walk(self.tmp_dir+'/output') for f in files])
test_files = sorted([os.path.join(r,f) for r,_,files in os.walk(self.test_path+'/output') for f in files])
for i,f in enumerate(res_files):
t1 = FileHandler.openNetCDFFile(res_files[i])
t2 = FileHandler.openNetCDFFile(test_files[i])
# print res_files[i]
# print test_files[i]
np.testing.assert_array_almost_equal(t1['variable'], t2['variable'], 3)
#elf.assertTrue((t1['variable']==t2['variable']).all())
self.assertTrue((t1['lon']==t2['lon']).all())
self.assertTrue((t1['lat']==t2['lat']).all())
def testMaxLeadYearHistorical(self):
msssDict = self.msssDict()
msssDict['decadals'] = range(1990,2000,5)
msssDict['project2'] = 'cmip5'
msssDict['leadtimes'] = '1,2'
msssDict['experiment2'] = 'historical'
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
def testMaxLeadYearHistorical(self):
msssDict = self.msssDict()
msssDict['decadals'] = range(1990, 2000, 5)
msssDict['project2'] = 'cmip5'
msssDict['leadtimes'] = '1,2'
msssDict['experiment2'] = 'historical'
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
def testSameModels(self):
msssDict = self.msssDict()
msssDict['project2'] = msssDict['project1']
msssDict['product2'] = msssDict['product1']
msssDict['experiment2'] = msssDict['experiment1']
msssDict['experiment2'] = msssDict['experiment1']
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
def testSameModels(self):
msssDict = self.msssDict()
msssDict['project2'] = msssDict['project1']
msssDict['product2'] = msssDict['product1']
msssDict['experiment2'] = msssDict['experiment1']
msssDict['experiment2'] = msssDict['experiment1']
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
def testEnsemblemembers(self):
msssDict = self.msssDict()
msssDict['ensemblemembers1'] = 'r1i1p1'
msssDict['ensemblemembers2'] = 'r2i1p1,r3i1p1'
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
for year in msssDict['decadals']:
self.assertTrue(len(msss.ensList[year]) == 1, 'Too many ensemblemembers found!')
self.assertTrue(len(msss.histList[year]) == 2, 'Too many ensemblemembers found!')
def testMsss(self):
msssDict = self.msssDict()
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
#test file names
res_files = [
f for r, _, files in os.walk(self.tmp_dir + '/output')
for f in files
]
test_files = [
f for r, _, files in os.walk(self.test_path + '/output')
for f in files
]
self.assertEqual(sorted(res_files), sorted(test_files))
res_files = [
f for r, _, files in os.walk(self.tmp_dir + '/plots')
for f in files
]
test_files = [
f for r, _, files in os.walk(self.test_path + '/plots')
for f in files
]
self.assertEqual(sorted(res_files), sorted(test_files))
#test values of all .nc files
res_files = sorted([
os.path.join(r, f)
for r, _, files in os.walk(self.tmp_dir + '/output') for f in files
])
test_files = sorted([
os.path.join(r, f)
for r, _, files in os.walk(self.test_path + '/output')
for f in files
])
#test_files = sorted([os.path.join(r,f) for r,_,files in os.walk(tets_p) for f in files])
# print len(res_files)
# print len(test_files)
for i, f in enumerate(res_files):
tmp_test = cdo.copy(input=test_files[i], options='-f nc')
t1 = FileHandler.openNetCDFFile(res_files[i])
t2 = FileHandler.openNetCDFFile(tmp_test) #test_files[i])
# print '_______________________________________-'
# print res_files[i]
# print test_files[i]
np.testing.assert_array_almost_equal(t1['variable'],
t2['variable'], 0)
self.assertTrue((t1['lon'] == t2['lon']).all())
self.assertTrue((t1['lat'] == t2['lat']).all())
def testEnsemblemembers(self):
msssDict = self.msssDict()
msssDict['ensemblemembers1'] = 'r1i1p1'
msssDict['ensemblemembers2'] = 'r2i1p1,r3i1p1'
msss = Msss(**msssDict)
msss.prepareInput()
msss.analyze()
for year in msssDict['decadals']:
self.assertTrue(
len(msss.ensList[year]) == 1,
'Too many ensemblemembers found!')
self.assertTrue(
len(msss.histList[year]) == 2,
'Too many ensemblemembers found!')
def testTestfiles(self):
from create_testfiles import createMsssInput
std1_goal = 1.
std2_goal = 2.
corr_goal = 0.5
std_ratio = std2_goal / std1_goal
cond_bias = corr_goal - std1_goal / std2_goal
msss_goal = corr_goal**2 + cond_bias**2
length = 50
(hindcasts, observations) = createMsssInput(1960, std1_goal, std2_goal,
corr_goal, length)
msssDict = self.msssDict()
msssDict['decadals'] = hindcasts.keys()
msssDict['observation'] = 'hadcrut3v'
msssDict['leadtimes'] = '1'
msss = Msss(**msssDict)
msss.input1Remapped = hindcasts
msss.input2Remapped = hindcasts
msss.observationRemapped = observations
msss.analyze()
goal_dict = {
'msss_goal':
'1-1/baseline1_output_mpi-esm-lr_decs4e/msss/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_msss.nc',
'corr_goal':
'1-1/baseline1_output_mpi-esm-lr_decs4e/msss/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_correlation.nc',
'cond_bias':
'1-1/baseline1_output_mpi-esm-lr_decs4e/msss/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_conditional_bias.nc',
'std_ratio':
'1-1/baseline1_output_mpi-esm-lr_decs4e/msss/1_1_tas_baseline1_output_mpi-esm-lr_decs4e_1960-2009_std_ratio.nc'
}
for key, val in goal_dict.iteritems():
t1 = FileHandler.openNetCDFFile(msss.outputDir + val, mode='var')
print key
print locals()[key]
print t1[12, 12]
self.assertAlmostEqual(np.round(t1[12, 12], 2), locals()[key], 2)
def main(argv=None): # IGNORE:C0111
'''Command line options.'''
if argv is None:
argv = sys.argv[1:]
args, lastargs = getopt.getopt(argv, _short_args, _args)
for flag,arg in args:
if flag=='-h' or flag=='--help':
return printHelp()
murcss_dict = getMurcssDict()
for arg in lastargs:
if '=' not in arg:
raise CommandError("Invalid format for query: %s" % arg)
items = arg.split('=')
key, value = items[0], ''.join(items[1:])
murcss_dict[key] = value
murcss_dict['decadals'] = map(int,murcss_dict['decadals'].split(','))
murcss_dict['bootstrap_number'] = int(murcss_dict['bootstrap_number'])
analysis_type = murcss_dict.pop('analysis_type')
if analysis_type == 'map':
murcss_dict['fieldmean'] = False
murcss_dict['zonalmean'] = False
elif analysis_type == 'fieldmean':
murcss_dict['fieldmean'] = True
murcss_dict['zonalmean'] = False
elif analysis_type == 'zonalmean':
murcss_dict['fieldmean'] = False
murcss_dict['zonalmean'] = True
output_type = murcss_dict.pop('output_type')
if output_type == 'basic':
murcss_dict['basic_output'] = True
else:
murcss_dict['basic_output'] = False
#Check for right metics value
metrics = murcss_dict.pop('metrics')
if metrics not in ['all','accuracy','ensemble_spread']:
raise Exception, '%s is not a valid option. Only valid options for metric are: "all", "ensemble_spread", and "accuracy".' % (metrics)
if metrics in ['all','accuracy']:
print '#######################'
print 'Calculating the MSSS'
print '#######################'
#Calculation of MSESS
if(not murcss_dict['significance']):
msss_dict = murcss_dict.copy()
msss_dict.pop('bootstrap_number')
msss_dict.pop('significance')
msss = Msss(**msss_dict)
msss.prepareInput()
msss.analyze()
msss.deleteCache()
else:
from metrics.msssBootstrap import main
msss = main(murcss_dict.copy(),'..')
if metrics in ['all','ensemble_spread']:
print '#######################'
print 'Calculating the CRPSS for Model1'
print '#######################'
crpss1 = Crpss(output=murcss_dict['output'],
output_plots=murcss_dict['output_plots'],
basic_output=murcss_dict['basic_output'],
decadals=murcss_dict['decadals'],
variable=murcss_dict['variable'],
project = murcss_dict['project1'],
product1=murcss_dict['product1'],
institute1=murcss_dict['institute1'],
model=murcss_dict['model1'],
experiment=murcss_dict['experiment1'],
ensemblemembers=murcss_dict['ensemblemembers1'],
observation = murcss_dict['observation'],
leadtimes=murcss_dict['leadtimes'],
result_grid=murcss_dict['result_grid'],
maskMissingValues=murcss_dict['maskMissingValues'],
bootstrapSwitch=murcss_dict['significance'],
bootstrap_number=murcss_dict['bootstrap_number'],
level=murcss_dict['level'],
lonlatbox=murcss_dict['lonlatbox'],
fieldmean=murcss_dict['fieldmean'],
zonalmean=murcss_dict['zonalmean'],
cache=murcss_dict['cache'],
baseDir = murcss_dict['baseDir'])
if metrics != 'ensemble_spread':
crpss1.outputDir = msss.outputDir
crpss1.outputPlots = msss.outputPlots
crpss1.prepareInput()
crpss1.analyze()
crpss1.deleteCache()
print '#######################'
print 'Calculating the CRPSS for Model2'
print '#######################'
crpss2 = Crpss(output=murcss_dict['output'],
output_plots=murcss_dict['output_plots'],
basic_output=murcss_dict['basic_output'],
decadals=murcss_dict['decadals'],
variable=murcss_dict['variable'],
project = murcss_dict['project2'],
product1=murcss_dict['product2'],
institute1=murcss_dict['institute2'],
model=murcss_dict['model2'],
experiment=murcss_dict['experiment2'],
ensemblemembers=murcss_dict['ensemblemembers2'],
observation = murcss_dict['observation'],
leadtimes=murcss_dict['leadtimes'],
result_grid=murcss_dict['result_grid'],
maskMissingValues=murcss_dict['maskMissingValues'],
bootstrapSwitch=murcss_dict['significance'],
bootstrap_number=murcss_dict['bootstrap_number'],
level=murcss_dict['level'],
lonlatbox=murcss_dict['lonlatbox'],
fieldmean=murcss_dict['fieldmean'],
zonalmean=murcss_dict['zonalmean'],
cache=murcss_dict['cache'],
input_part='input2',
baseDir = murcss_dict['baseDir'])
if metrics != 'ensemble_spread':
crpss2.outputDir = msss.outputDir
crpss2.outputPlots = msss.outputPlots
crpss2.prepareInput()
crpss2.analyze()
crpss2.deleteCache()
import metrics.msss, metrics.metricAbstract, metrics.filehandler, metrics.taylorplot, metrics.crpss, metrics.msssBootstrap
del metrics.msss.cdo, metrics.metricAbstract.cdo, metrics.filehandler.cdo, metrics.taylorplot.cdo, metrics.crpss.cdo, metrics.msssBootstrap.cdo
print 'Calculation finished.'
print 'Plots produced in %s' %(msss.outputPlots,)